The land surface soil moisture is a crucial variable in weather and climate models. This study presents a land data assimilation system (LDAS) that aims to improve the simulation of the land surface soil moisture and energy fluxes bymerging themicrowave remote sensing data and the general circulationmodel (GCM)output into a land surfacemodel (LSM). This systemwas applied over the TibetanPlateau...

Soil moisture is a key variable for the water and energy exchanges at the land surface. The determination of soil moisture dynamics from space is one of the most prominent, but also most challenging applications for recent active microwave sensor systems. Since the launch of ENVISAT ASAR, more than 4 years of data is available for the retrieval of soil moisture information. The wide area covera...

The authors evaluated several land surface variables from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product that are important for global ecological and hydrological studies, including daily maximum (Lnax) and minimum (Lnin) surface air temperatures, atmosphere vapor pressure deficit (VPD), incident solar radiation (SWrad), and surface soil moisture. The M ERRA...

E€ective control of nonpoint source pollution from contaminants transported by runo€ requires information about the source areas of surface runo€. Variable source hydrology is widely recognized by hydrologists, yet few methods exist for identifying the saturated areas that generate most runo€ in humid regions. The Soil Moisture Routing model is a daily water balance model that simulates the hyd...

The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range ...

Surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals even though they typically exhibit very different mean values and variability. At the global scale, in particular, it is currentl...

Soil moisture can significantly influence atmospheric evolution. However the soil moisture state predicted by land surface models, and subsequently used as the boundary condition in atmospheric models, is often unrealistic. New remote sensing technologies are able to observe surface soil moisture at the scales and coverage required by numerical weather prediction (NWP), and there is potential t...

[1] The Kalman filter data assimilation technique is applied to a distributed threedimensional soil moisture model for retrieval of the soil moisture profile in a 6 ha catchment using near-surface soil moisture measurements. A simplified Kalman filter covariance forecasting methodology is developed based on forecasting of the state correlations and imposed state variances. This covariance forec...